천리안 기상위성을 이용한 한반도 지역의 Linke turbidity 및 청천일사량 추정

송아람,최강혁,정민경,김용일 한국신·재생에너지학회 2016 신재생에너지 Vol.12 No.S1

An estimation of the clear sky irradiance is a crucial part of satellite based methods because it is employed to calculatethe clear sky index. Although the accuracy of the clear sky irradiance depends on the AOD (Aerosol Optical Depth) and TPW (TotalPrecipitable Water) mixing ratio, data are difficult to acquire in real time. The Linke turbidity factor simplifies the data as a uniqueparameter that describes the attenuation of solar radiation in terms of a clean and dry atmosphere. SoDa provides the Linke turbiditymaps all over the world, but those maps have low spatial and temporal resolutions. To estimate the clear sky irradiance over theKorean Peninsula using satellite images, this paper presents a method to estimate the Linke turbidity factor using COMS MI, which isoperated by the Korea Meteorological Administration, and the clear sky irradiance using the ESRA clear sky model. The AOD and theTPW derived from COMS MI were also used to calculate the Linke turbidity. Overall, the results show that the Linke turbidity factorcalculated from COMS MI has higher accuracy than that calculated using the SoDa data.

개방형 다중 데이터셋을 활용한 Combined Segmentation Network 기반 드론 영상의 의미론적 분할

송아람,Ahram Song 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5

This study proposed and validated a combined segmentation network (CSN) designed to effectively train on multiple drone image datasets and enhance the accuracy of semantic segmentation. CSN shares the entire encoding domain to accommodate the diversity of three drone datasets, while the decoding domains are trained independently. During training, the segmentation accuracy of CSN was lower compared to U-Net and the pyramid scene parsing network (PSPNet) on single datasets because it considers loss values for all dataset simultaneously. However, when applied to domestic autonomous drone images, CSN demonstrated the ability to classify pixels into appropriate classes without requiring additional training, outperforming PSPNet. This research suggests that CSN can serve as a valuable tool for effectively training on diverse drone image datasets and improving object recognition accuracy in new regions.

Measurement of Biologically Active on oyster hydrolyzed using for enzyme

송아람,우영민,박승후,하종명,김안드레 한국공업화학회 2015 한국공업화학회 연구논문 초록집 Vol.2015 No.0

Oysters are foods that contain a variety of nutrients called milk of the sea. Vitamins, calcium, iron and the like containing various nutrients.Enzyme used is Protamex (P), Alcalase (A), Neutrase (N), bromelain (B),was used as a transglutaminase (T). These are different from the reaction temperature and pH. DPPH radical scavenging activity of the positive control, and A is the BHT 93.65% 52.33% and 84.65% and the TA AN showed the scavenging by 51.83%. At this time, the concentration of the sample was used as 250~1000 mg / ml. In den by the same result it can be seen that has a bioactive.

Surface Water Mapping of Remote Sensing Data Using Pre-Trained Fully Convolutional Network

송아람,정민영,김용일 한국측량학회 2018 한국측량학회지 Vol.36 No.5

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.

분자개념을 도입한 Peer Instruction 수업이 5학년 학생의 물질의 상태 분류 능력 및 기준에 미치는 영향

송아람,양성호 한국현장과학교육학회 2018 현장과학교육 Vol.12 No.3

Peer instruction (PI) based on the molecular concept influenced 5th grade students’understanding on states of matter. After selecting some materials causing cognitive conflict among states of matter (solid, liquid, gas, mixture), we examined effect of 3 teaching methods on classification of matters: PI based on the molecular concept, PI without teaching molecular concept, and the traditional teaching methods introducing the molecular concept. PI based on the molecular concept was the most effective in enhancing classification of matters and changed the criteria on classification of matters from macroscopic view to microscopic view. Particularly, in the post test two weeks later, the group applied PI based on the molecular concept still maintained classifying ability better than control groups. The result is explained by the fact that the molecular concept was actively used as a material for discussion in PI. Generally, it needs conceptual materials for efficient discussion in PI. 분자개념을 도입한 Peer Instruction(PI) 수업이 초등학교 5학년 학생들의 물질의 상태 분류 능력과 분류 기준에 어떠한 영향을 미치는지 조사하였다. 고제, 액체, 기체, 혼합물 등으로 물질의 상태를 분류하기 어려워하는 사물들을 선별하여, 분자개념 도입 혹은 PI 적용 여부에 따라 물질의 상태 분류 능력이 어떠한 차이를 보이는지를 분석하였다. 분자개념을 도입한 PI 수업이 분자 개념 혹은 PI만을 적용한 수업에 비해 학생들의 분류 능력을 크게 향상시켰다는 사실과 분류 기준이 거시적 관점에서 미시적 과점으로 변화한다는 사실을 확인하였다. 특히, 2주 후 실시된 사후테스트에서도 분자개념을 도입한 PI 수업 적용 집단의 분류 능력이 대조집단에 비하여 유의미하게 높게 유지되었다. 이러한 결과는 PI 수업 전에 획득된 분자 개념이 소집단 토론 중에 활발히 활용되어서 학생들의 학습에 큰 도움을 주었기 때문으로 설명하였다. PI와 같은 소집단 토론으로 학습 효과가 향상되기 위해서는 토론에서 활용할 개념적 소재가 필요함을 알 수 있다. 개념의 이해가 중요한 물리 교육에 많이 사용된 PI를 화학 교육에 효과적으로 적용하기 위해서는 개념적 토대가 되는 분자 개념의 제시가 효과적일 것으로 예상한다.

Semantic Segmentation of Heterogeneous Unmanned Aerial Vehicle Datasets Using Combined Segmentation Network

송아람 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.1

Unmanned aerial vehicles (UAVs) can capture high-resolution imagery from a variety of viewingangles and altitudes; they are generally limited to collecting images of small scenes from larger regions. Toimprove the utility of UAV-appropriated datasets for use with deep learning applications, multiple datasets createdfrom various regions under different conditions are needed. To demonstrate a powerful new method for integratingheterogeneous UAV datasets, this paper applies a combined segmentation network (CSN) to share UAVid andsemantic drone dataset encoding blocks to learn their general features, whereas its decoding blocks are trainedseparately on each dataset. Experimental results show that our CSN improves the accuracy of specific classes(e.g., cars), which currently comprise a low ratio in both datasets. From this result, it is expected that the rangeof UAV dataset utilization will increase.

핵 활동 탐지 및 감시를 위한 딥러닝 기반 의미론적 분할을 활용한 변화 탐지

송아람,이창희,이진민,한유경,Song, Ahram,Lee, Changhui,Lee, Jinmin,Han, Youkyung 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

지상용 초분광 스캐너를 활용한 사과의 당도예측 모델의 성능향상을 위한 연구

송아람,전우현,김용일,Song, Ahram,Jeon, Woohyun,Kim, Yongil 대한원격탐사학회 2017 大韓遠隔探査學會誌 Vol.33 No.5

본 연구에서는 야외에서 자료 취득이 가능하며 한 번에 다량의 사과를 촬영할 수 있는 지상용 초분광 스캐너를 활용하여 사과의 분광정보와 당도와의 부분최소제곱회귀분석(PLSR, Partial Least Square Regression)을 수행하였으며, 최적의 예측모델을 구축하기 위한 다양한 전처리기법의 적용가능성을 평가하고 VIP(Variable Importance in Projection)점수를 통한 최적밴드를 산출하였다. 이를 위하여 360-1019 nm영역에서 촬영된 515밴드의 초분광 영상에서 70개의 분광곡선을 취득하였으며, 디지털광도계를 이용하여 당도($^{\circ}Brix$)를 측정하였다. 사과의 분광특성과 당도사이의 회귀모델을 구축하였으며, 최적의 예측모델은 모델 예측치와 실측치간의 결정계수($r_p^2$, coefficient of determination of prediction)와 RMSECV(Root Mean Square Error of Cross Validation), RMSEP(Root Mean Square Error of Prediction)등을 고려하여 선정하였다. 그 결과 산란보정 기법의 대표적인 MSC(Multiplicative Scatter Correction)의 기반의 전처리기법이 가장 효과적이었으며, MSC와 SNV(Standard Normal Variate)를 조합한 경우 RMSECV와 RMSEP가 각각 0.8551과 0.8561로 가장 낮았고, $r_c^2$와 $r_p^2$은 각각 0.8533과 0.6546으로 가장 높았다, 또한 360-380, 546-690, 760, 915, 931-939, 942, 953, 971, 978, 981, 988, 992-1019 nm 등이 당도 측정을 위한 가장 영향력 있는 파장영역으로 나타났다. 해당 영역의 분광값을 가지고 PLSR을 수행한 결과, 전파장대를 사용할 때보다 RMSEP가 0.6841로 감소하고 $r_p^2$는 0.7795로 증가하는 것을 확인하였다. 본 연구를 통하여 사과의 당도측정에 있어 야외에서 취득한 초분광 영상자료의 활용 가능성을 확인하였으며, 이는 필드자료 및 센서 활용분야의 확장가능성을 보여준다. A partial least squares regression (PLSR) model was developed to map the internal soluble solids content (SSC) of apples using a ground-based hyperspectral scanner that could simultaneously acquire outdoor data and capture images of large quantities of apples. We evaluated the applicability of various preprocessing techniques to construct an optimal prediction model and calculated the optimal band through a variable importance in projection (VIP)score. From the 515 bands of hyperspectral images extracted at wavelengths of 360-1019 nm, 70 reflectance spectra of apples were extracted, and the SSC ($^{\circ}Brix$) was measured using a digital photometer. The optimal prediction model wasselected considering the root-mean-square error of cross-validation (RMSECV), root-mean-square error of prediction (RMSEP) and coefficient of determination of prediction $r_p^2$. As a result, multiplicative scatter correction (MSC)-based preprocessing methods were better than others. For example, when a combination of MSC and standard normal variate (SNV) was used, RMSECV and RMSEP were the lowest at 0.8551 and 0.8561 and $r_c^2$ and $r_p^2$ were the highest at 0.8533 and 0.6546; wavelength ranges of 360-380, 546-690, 760, 915, 931-939, 942, 953, 971, 978, 981, 988, and 992-1019 nm were most influential for SSC determination. The PLSR model with the spectral value of the corresponding region confirmed that the RMSEP decreased to 0.6841 and $r_p^2$ increased to 0.7795 as compared to the values of the entire wavelength band. In this study, we confirmed the feasibility of using a hyperspectral scanner image obtained from outdoors for the SSC measurement of apples. These results indicate that the application of field data and sensors could possibly expand in the future.

박물관 공간에 나타난 현상학적 특성에 관한 연구

송아람(Song, Ah-Ram),김문덕(Kim, Moon-Duck) 한국실내디자인학회 2007 한국실내디자인학회 학술대회논문집 Vol.9 No.3

비트코인 믹싱 프로토콜 현황 조사

송아람(Aram Song),이유정(Yujeong Lee),정다은(Daeun Jeong),박수용(Sooyong Park) 한국정보과학회 2020 한국정보과학회 학술발표논문집 Vol.2020 No.7